
NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
Recipient: |
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Initial Amendment Date: | August 21, 2019 |
Latest Amendment Date: | August 21, 2019 |
Award Number: | 1925085 |
Award Instrument: | Standard Grant |
Program Manager: |
Jordan Berg
jberg@nsf.gov (703)292-5365 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
Start Date: | September 1, 2019 |
End Date: | August 31, 2023 (Estimated) |
Total Intended Award Amount: | $1,499,696.00 |
Total Awarded Amount to Date: | $1,499,696.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
1033 MASSACHUSETTS AVE STE 3 CAMBRIDGE MA US 02138-5366 (617)495-5501 |
Sponsor Congressional District: |
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Primary Place of Performance: |
60 Oxford Street Cambridge MA US 02138-5369 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | Special Initiatives |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.041 |
ABSTRACT
This project will advance the progress of customizable soft robotic exosuits for seamless assistance in everyday activities, to mitigate impairment or to augment normal functionality. Soft robotic exosuits are a new class of functional clothing that apply mechanical assistance to wearers' joints in parallel with their muscles. Millions of Americans with neurologically-based walking impairments, such as the nearly seven million people living post-stroke, could benefit from the unobtrusive assistance that exosuits can provide during walking. Similarly, healthy individuals who carry heavy loads over long distances (e.g., first responders or soldiers) could benefit from using exosuit technology to partially alleviate their burden. Synchronizing mechanical assistance to the wearer's natural rhythm is essential to realizing the potential of these devices. The soft robotic exosuits created in this project will monitor the wearer's walking pattern using body-worn sensors, apply machine learning methods to personalize the robotic assistance pattern, and continuously update that assistance pattern as the wearer's gait changes. In contrast, current methods rely on expert clinicians and technicians to manually tune assistance patterns. This project will contribute new knowledge to advance the national health and prosperity. The multidisciplinary research team includes roboticists, movement scientists, and clinicians, who will work closely with persons poststroke in laboratory and clinical environments.
Recent work developing human-in-the loop optimization strategies for exoskeletons suggests that it is possible to lower the metabolic cost of walking by treating the exosuit control as an optimization problem, with direct measurements of metabolic cost serving as the objective. However, recording such measurements require bulky devices that interfere with normal breathing, meaning the method cannot currently work in everyday environments. Additionally, lowering the metabolic cost of walking is not the only goal of exosuits in persons poststroke, whose gait is characterized by slow movement and compensations such as hip hiking, circumduction, and vaulting. Successful human-in-the-loop optimization in clinical populations will need to address all these factors, not just metabolic cost. An initial basic-science exploration will find effective proxies for metabolic cost in healthy populations and develop a multi-objective function in people poststroke. In both cases, the objective function must be unobtrusive to measure, accurate, and responsive to changes in exosuit control. A second implementation phase will use human-in-the-loop optimization to automatically adapt control parameters of portable systems assisting healthy individuals and people poststroke.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Over the past decade, significant advances have been made in wearable robotic technology with systems showing promising results in augmenting human locomotion and restoring impaired human mobility. An exciting and very recent development in the field has been the application of techniques that enable the individualization of assistance. In the project we advance this area and also demonstrate how it enables evaluation of wearable robotic systems in the community. In this project, we showed that we could use human in the loop optimization for a hip flexion exosuit to augment locomotion in healthy individuals (i.e., reduce the energy expenditure of human walking). In addition, we demonstrated that we could use non-invasive ultrasound imaging to monitoring muscle activity during walking and individualize assistance from an ankle exosuit. We found that by monitoring muscle activity with ultrasound we could individualize assistance for an ankle exosuit and achieve high metabolic reductions with relative low applied force.
Intellectual Merit: We performed a number of human subjects studies as part of this project with a variety of different populations. We evaluated the effects of a portable ankle exosuit during continuous comfortable overground walking in 19 individuals with chronic hemiparesis. Exosuit assistance was associated with improvements in the targeted gait impairments: 22% increase in ground clearance during swing, 5° increase in foot-to-floor angle at initial contact, and 22% increase in the center-of mass propulsion during push-off. The improvements in propulsion and foot landing contributed to a 6.7% (0.04 m/s) increase in walking speed (R2 = 0.82). Subgroup analyses revealed that all individuals profited from ground clearance support, but slower individuals leveraged plantarflexor assistance to improve propulsion by 35% to walk 13% faster, while faster individuals did not change either. We also developed and evaluated a community Robotic Exosuit Augmented Locomotion (cREAL) program. Four participants in the chronic stage of stroke independently used our community ankle exosuit for walking in the community 3–5 days/week for 4 weeks. We performed lab evaluations before and after the 4-week program. Two participants significantly improved their unassisted paretic propulsion by an average of 27%after the program and walked on average 4001 steps/day more in the week following the program. Despite the small number of participants, this study provides preliminary evidence for the potential of exosuits to augment gait training and rehabilitation in the community. Using a hip exosuit, in an n-of-1 trial with five repeated measurements spanning 6 months, we evaluated impact of robotic assistance on a 73-year-old male with Parkinson’s disease and substantial freezing of gait. With assistance, FoG was instantaneously eliminated during indoor walking (0% versus 39 ± 16% time spent freezing when unassisted), accompanied by 49 ± 11 m (+55%) farther walking compared to unassisted walking, faster speeds (+0.18 m s−1) and improved gait quality (−25% in gait variability). FoG-targeting effects were repeatable across multiple days, provoking conditions and environment contexts, demonstrating potential for community use.
Broader Impacts: The proposed research has the potential to enable wearable robots to assist a wide range of people in real world settings, enabling them to be ubiquitously deployed and customized to different user needs. The methods from this research will be suitable for individuals engaged in physically strenuous walking activities as well as other populations besides stroke survivors such as individuals with Parkinson’s Disease, Multiple Sclerosis or the elderly. Moreover, this research will also be valuable to community-based rehabilitation programs broadly where the estimation algorithms we develop can be used to remotely monitor performance and recovery using wearable sensors. Our team continued to support the Bionic5k, an inclusive event and running race that is for adaptive athletes, all runners, and weekend warriors to celebrates technology, sport, and the human spirit. We also developed a diversity-promoting National Biomechanics Day event for local high school students that may not typically have exposure to biomechanics research and ensure that school districts interested in participating would have the necessary support.
Last Modified: 01/18/2024
Modified by: Conor Walsh
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